Digital exclusion and people experiencing homelessness: implications for opioid use disorder care
Bibliographic record
Abstract
PURPOSE OF REVIEW: People experiencing homelessness (PEH) are at increased risk of adverse consequences from opioid use disorder and other health conditions yet face multiple structural and personal barriers to accessing care. The expansion of digitized health and social care services may have improved access and efficiency of services to many in the general population but at the cost of further marginalizing PEH. Current digital exclusion mitigation strategies may not be sufficiently nuanced to address the deeply complex and challenging circumstances of PEH lives. RECENT FINDINGS: Providing devices, data and skills to PEH is no guarantee of increased use and benefit from digitally enabled services. Precarious and constantly mobile lives mean that maintaining sustained digital access is problematic and not always desirable. Even where digital access is secured, PEH are constrained in the range of activities they can engage with online due to privacy and other structural constraints. Justifiable distrust of institutions including healthcare colors the acceptability of digitized services for PEH. This distrust is magnified due to new inequities and vulnerabilities introduced by digitized services including the need for a digital persona, adverse outcomes from adverse digital inclusion and a widening of power imbalances. These more nuanced understandings of digital exclusion are increasingly incorporated into mitigation strategies, premised on co-production and engagement with PEH. SUMMARY: Improved engagement with digitally enabled OUD care for PEH must be prefaced by improved access to technology, optimized physical environments to maintain and use technology, and collaborative cross-sectoral efforts to build trust and engage this group through co-production and rebalanced power dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".